pqm: Parametric Quantile Mapping method for bias correction

In SantanderMetGroup/downscaleR: A climate4R package for statistical downscaling <http://meteo.unican.es/climate4r>

Parametric Quantile Mapping method for bias correction

Description

Implementation of Parametric Quantile Mapping method for bias correction

Usage

pqm(o, p, s, fitdistr.args, precip, pr.threshold)

Arguments

o	A vector (e.g. station data) containing the observed climate data for the training period
p	A vector containing the simulated climate by the model for the training period.
s	A vector containing the simulated climate for the variable used in x, but considering the test period.
fitdistr.args	Further arguments passed to function fitdistr (densfun, start, ...). Only used when applying the "pqm" method (parametric quantile mapping). Please, read the fitdistr help document carefully before parameter setting in fitdistr.args.
precip	Logical for precipitation data. If TRUE Adjusts precipitation frequency in 'x' (prediction) to the observed frequency in 'y'. This is a preprocess to bias correct precipitation data following Themeßl et al. (2012). To adjust the frequency, parameter pr.threshold is used (see below).
pr.threshold	The minimum value that is considered as a non-zero precipitation. Ignored when precip = FALSE. See details in function biasCorrection.

Author(s)

S. Herrera and M. Iturbide

SantanderMetGroup/downscaleR documentation built on July 4, 2023, 4:28 a.m.